{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 计算脑区内投射"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取nrrd,swc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import swcloader\n",
    "import nrrd\n",
    "neuron =swcloader.NeuronTree(\"033.swc\")\n",
    "annotation,option = nrrd.read(\"annotation_10_2017.nrrd\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(annotation) )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 解析annot.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['', 'root']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "lines=[]\n",
    "regions={}\n",
    "\n",
    "with open(\"annot.txt\", 'r') as file_to_read:\n",
    "#   while True:\n",
    "    lines = file_to_read.readlines()\n",
    "for line in lines:\n",
    "    linetmp=line[:-1].split(\":\")\n",
    "    regions[int(linetmp[0])]=[linetmp[1],linetmp[2]]\n",
    "print(regions[997])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算脑区投射"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# neuron1 =swcloader.NeuronTree(\"033.swc\")\n",
    "neuron192092012 =swcloader.NeuronTree(\"192092-012.swc\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1320, 800, 1140)\n",
      "1,1,3263.94706,2135.46634,6073.39371,1.75,-1\n",
      "{'ACAd5': 783.3125809905259, 'ACAd6a': 2114.0968428054935, 'PL6a': 107.16547681318156, 'PL6b': 68.80871093164357, 'cing': 380.06092963063185, 'fa': 2650.280516301974, 'MOs6a': 1279.6760339511843, 'CP': 6837.750079689032, 'ccg': 2002.582361179783, 'ACAv5': 16.526971611592295, 'ACAv6a': 15.303874849145302, 'FRP6a': 253.6606722364347, 'ORBl6a': 1014.6183965989841, 'AId6a': 1423.6876077807224, 'AId5': 376.3731736823934, 'MOs6b': 33.16151381325206, 'scwm': 271.81167076316046, 'AId6b': 66.38369338782428, 'CLA': 486.4672399459744}\n",
      "20181.728346962864\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "import math\n",
    "print(annotation.shape)\n",
    "annotation[0,0,0]\n",
    "\n",
    "regionLength={}\n",
    "edgeIndex=0\n",
    "root=neuron192092012.root\n",
    "print(root)\n",
    "regionID = annotation[int(root.x/10),int(root.y/10),int(root.z/10)]\n",
    "regionName=regions[int(regionID)][1]\n",
    "\n",
    "\n",
    "axonLength=0\n",
    "for edge in neuron192092012.edges:\n",
    "    if edgeIndex>=0:\n",
    "        pointIndex=0\n",
    "        start=edge[0]\n",
    "        for point in edge:\n",
    "            if pointIndex>0:\n",
    "                end=point\n",
    "                length = math.sqrt((start.x-end.x)**2+(start.y-end.y)**2+(start.z-end.z)**2)\n",
    "                axonLength+=length\n",
    "                regionID = annotation[int(end.x/10),int(end.y/10),int(end.z/10)]\n",
    "                regionName=regions[int(regionID)][1]\n",
    "\n",
    "                if(regionName not in regionLength):\n",
    "                    regionLength[regionName]=0\n",
    "                regionLength[regionName]+=length\n",
    "            start=point\n",
    "            pointIndex+=1\n",
    "\n",
    "        pass\n",
    "    edgeIndex+=1\n",
    "print(regionLength)\n",
    "print(axonLength)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算脑区内terminal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28\n",
      "{'ACAd5': 5, 'ACAd6a': 4, 'MOs6a': 2, 'CP': 13, 'AId6b': 1, 'AId6a': 2, 'CLA': 1}\n"
     ]
    }
   ],
   "source": [
    "regionTerminal={}\n",
    "print(len(neuron192092012.terminals))\n",
    "for point in neuron192092012.terminals:\n",
    "#     print(point)\n",
    "    regionID = annotation[int(point.x/10),int(point.y/10),int(point.z/10)]\n",
    "    regionName=regions[int(regionID)][1]\n",
    "    if(regionName not in regionTerminal):\n",
    "        regionTerminal[regionName]=0\n",
    "    regionTerminal[regionName]+=1\n",
    "print(regionTerminal) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 画图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 常用包matplotlib、pandas、seaborn、ggplot(R)、bokeh、plotly、vispy等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# https://zhuanlan.zhihu.com/p/390208274\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### matplotlib、pandas、seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "import seaborn as sns\n",
    "import palettable\n",
    " \n",
    "plt.rcParams['font.sans-serif']=['SimHei']  # 用于显示中文\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用于显示中文\n",
    " \n",
    "from sklearn import datasets \n",
    "iris=datasets.load_iris()\n",
    "x, y = iris.data, iris.target\n",
    "pd_iris = pd.DataFrame(np.hstack((x, y.reshape(150, 1))),columns=['sepal length(cm)','sepal width(cm)','petal length(cm)','petal width(cm)','class'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sepal length(cm)  sepal width(cm)  petal length(cm)  petal width(cm)  \\\n",
      "0                 5.1              3.5               1.4              0.2   \n",
      "1                 4.9              3.0               1.4              0.2   \n",
      "2                 4.7              3.2               1.3              0.2   \n",
      "3                 4.6              3.1               1.5              0.2   \n",
      "4                 5.0              3.6               1.4              0.2   \n",
      "..                ...              ...               ...              ...   \n",
      "145               6.7              3.0               5.2              2.3   \n",
      "146               6.3              2.5               5.0              1.9   \n",
      "147               6.5              3.0               5.2              2.0   \n",
      "148               6.2              3.4               5.4              2.3   \n",
      "149               5.9              3.0               5.1              1.8   \n",
      "\n",
      "     class  \n",
      "0      0.0  \n",
      "1      0.0  \n",
      "2      0.0  \n",
      "3      0.0  \n",
      "4      0.0  \n",
      "..     ...  \n",
      "145    2.0  \n",
      "146    2.0  \n",
      "147    2.0  \n",
      "148    2.0  \n",
      "149    2.0  \n",
      "\n",
      "[150 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "pd_iris.describe()\n",
    "print(pd_iris)\n",
    "#基本统计信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x2c0cdcff2b0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=200)\n",
    "sns.clustermap(data=pd_iris,#仅仅需传入绘图数据集\n",
    "              )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据获取及分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          192092002.swc  192092003.swc  192092004.swc  \\\n",
      "id                           181.000000     182.000000     183.000000   \n",
      "projectleftregion.ACAd5        0.000000    1689.351929    1430.283325   \n",
      "projectleftregion.ACAd6a     465.318237    2454.736572    2657.353516   \n",
      "projectleftregion.ACAd6b       0.000000      96.809433       0.000000   \n",
      "projectleftregion.CP        2366.737793    5544.711426    2176.005859   \n",
      "projectleftregion.MOs6a      640.979004     839.260681     350.426819   \n",
      "projectleftregion.PL6a       885.485107    2276.322021    1116.854858   \n",
      "projectleftregion.PL6b       107.840630      20.931618      93.961555   \n",
      "projectleftregion.ccg        686.805786    1884.784790    1186.077515   \n",
      "\n",
      "                          192092005.swc  192092006.swc  192092007.swc  \\\n",
      "id                                  184     185.000000     186.000000   \n",
      "projectleftregion.ACAd5               0    3687.767822    1202.338989   \n",
      "projectleftregion.ACAd6a              0    1264.749146    2503.009521   \n",
      "projectleftregion.ACAd6b              0      16.349419       0.000000   \n",
      "projectleftregion.CP                  0       0.000000    2123.724365   \n",
      "projectleftregion.MOs6a               0       0.000000     890.460632   \n",
      "projectleftregion.PL6a                0     958.219177     278.512970   \n",
      "projectleftregion.PL6b                0      90.828148      51.283180   \n",
      "projectleftregion.ccg                 0     852.714905    1160.359009   \n",
      "\n",
      "                          192092008.swc  192092009.swc  192092010.swc  \\\n",
      "id                                  187     188.000000     189.000000   \n",
      "projectleftregion.ACAd5               0    1410.125000    9298.387695   \n",
      "projectleftregion.ACAd6a              0    1094.204956    1731.488281   \n",
      "projectleftregion.ACAd6b              0      32.981754      26.980230   \n",
      "projectleftregion.CP                  0       0.000000    8101.200195   \n",
      "projectleftregion.MOs6a               0      95.079315       0.000000   \n",
      "projectleftregion.PL6a                0     442.562866     961.942017   \n",
      "projectleftregion.PL6b                0      38.335438     179.187622   \n",
      "projectleftregion.ccg                 0     798.471375     614.346069   \n",
      "\n",
      "                          192092011.swc  192092012.swc  192092013.swc  \\\n",
      "id                           190.000000     191.000000            192   \n",
      "projectleftregion.ACAd5     1814.466675       0.000000              0   \n",
      "projectleftregion.ACAd6a    4204.039551    1382.942749              0   \n",
      "projectleftregion.ACAd6b     141.815369      24.439289              0   \n",
      "projectleftregion.CP        5407.683105     386.112152              0   \n",
      "projectleftregion.MOs6a     3096.851807       2.164785              0   \n",
      "projectleftregion.PL6a         2.426461      21.253700              0   \n",
      "projectleftregion.PL6b        17.774368      37.748325              0   \n",
      "projectleftregion.ccg        921.434814    1066.957153              0   \n",
      "\n",
      "                          192092014.swc  \n",
      "id                           193.000000  \n",
      "projectleftregion.ACAd5        0.000000  \n",
      "projectleftregion.ACAd6a       0.000000  \n",
      "projectleftregion.ACAd6b       0.000000  \n",
      "projectleftregion.CP       12596.651367  \n",
      "projectleftregion.MOs6a     1330.508667  \n",
      "projectleftregion.PL6a         0.000000  \n",
      "projectleftregion.PL6b         0.000000  \n",
      "projectleftregion.ccg        912.088379  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas\n",
    "import seaborn as sns\n",
    "df = pandas.read_csv('192092.csv',index_col=0)\n",
    "df=df.fillna(0)\n",
    "df=df.iloc[11:20, 1:]\n",
    "# df= df.drop(['somaregion'])\n",
    "\n",
    "df = df.apply(pd.to_numeric, errors='coerce').fillna(0)\n",
    "print(df)\n",
    "\n",
    "g= sns.clustermap(df)\n",
    "# Hierarchical clustering\n",
    "\n",
    "# ax = g.ax_heatmap\n",
    "# # print(type(ax))\n",
    "# label_y = ax.get_yticklabels()\n",
    "# plt.setp(label_y, rotation=90, horizontalalignment='left')\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Site   Age\n",
      "0   2.0  10.0\n",
      "1   3.0  12.0\n",
      "2   1.0  13.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = [['2',10],['3',12],['1',13]]\n",
    "\n",
    "df = pd.DataFrame(data,columns=['Site','Age'],dtype=float)\n",
    "\n",
    "print(df)\n",
    "g= sns.clustermap(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
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